We report unweighted average recall (UAR) as our comparison measure. UAR is an
unweighted accuracy that gives the same weights to different classes and is a
popular metric for emotion recognition, used to account for unbalanced datasets [1]}.
We evaluate the performance of the methods using a repeated ten-fold cross-validation scheme, as used in [2]}.
The folds are stratified based on speaker ID. In each step of cross-validation, one
fold is used for testing, another is reserved for early stopping, and the remaining
eight folds are used for training. We repeat this evaluation scheme ten times, resulting in ten UARs for each iteration. We calculate the mean and standard deviation UAR within folds and report the mean of these statistics over all iterations. We perform significance tests using a repeated cross-validation paired \(t\) -test with ten degrees of freedom, as shown in [2]}, and note significance when \(p < 0.05\) .
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